1. Field of the Invention
Aspects of the present invention relate to a method of adaptively updating a recommend user group (RUG), and more particularly, to a method and apparatus to adaptively update an RUG that enables an active user to continuously keep a highly reliable list of RUGs by adaptively updating the RUG to anticipate the active user's contents preference. More specifically, aspects of the present invention relate to the field of a recommend system and a collaborative filtering. In particular, aspects of the present invention relate to a method of anticipating a contents preference to selectively provide users with useful information among a huge amount of multimedia contents (for example, culture contents including numerous broadcasting channels, movies, dramas, and music based on digital broadcasts and product contents based on home shopping).
2. Description of the Related Art
Due to an increase in an amount of contents, it is difficult for an individual to search for useful information regarding the contents. Accordingly, a variety of methods of anticipating a user's preferences for corresponding contents have been proposed. Of these methods, a method of generating a user group indicating similar preferences based on a user preference profile of a computer or terminal and receiving contents from users of a corresponding group is widely used.
FIG. 1 illustrates a method of classifying users in groups and recommending contents based on a server according to a conventional technology. Referring to FIG. 1, a customer database DB 110 to manage data of customers is provided in a web server providing contents (such as home shopping or news). In FIG. 1, the customer database DB 110 is a database including basic information of users and information on the user's activities on a corresponding web server. The groups 120 each signify a group of users having a similar preference that is generated based on the customer database DB 110. Specifically, a contents provider efficiently manages customers by dividing the customers into a variety of groups 120 using the customer database 110. When a customer 160 uses a corresponding server, the customer 160 accesses a network (such as an Internet web server of a contents provider) and inputs personal information. The contents provider analyzes the information and a record about the customer's activities and selects a group to which the customer 160 belongs in operation 130. Next, other customers who are more similar to the target customer 160 are searched for in a selected group by using a case-based inference or pattern-based inference in operation 140. Finally, a recommendation result is generated using products the similar customers purchased and provided to the target customer 160.
According to the above processes, a user accesses a web server through a login process by inputting an ID and a password. Furthermore, the user processes a series of operations according to the characteristics of the web server. For example, when accessing an e-commerce site such as AMAZON, the user searches for a desired item and performs a general process (such as a purchase or a check for a wish list) based on a result of the search. The web server may determine a propensity of a corresponding user by analyzing a series of a user's movements and actions in the above operations. For example, assuming that a site that the user accesses is an electronic commerce site, the propensity of the user may be determined based on information such as items the user searched for or items the user directly viewed, purchased, or added in a wish list from a search result.
The web server analyzes the propensity of all users registered on the web server and manages the users by classifying users having a similar propensity in groups 120. The users belonging to the web server are classified in each of the groups 120 through the above process. Then, the web server provides a service customized to each user according to the characteristic of the group to which the user belongs.
However, the above conventional recommendation system has the following problems. The conventional technology has a limitation of the web server. For example, when groups 120 are generated based on users belonging to a first web server, if the user accesses a second web server, another user group needs to be generated and data of the first web server is not sufficient. Also, according to the conventional technology, since the propensity of a user is determined based on the activities on the web server, not from a computer or a network terminal used by the user, the propensity of a user may not be appropriately reflected. For example, a web server providing personal services (such as blogs) provides the personal services by analyzing the preferences of a user who accesses the web server and directly selects items or other propensities of the user acting on the web server. However, the web server cannot analyze and reflect the items selected or activities performed by the user outside of the web server.